This section provides background information related to the present disclosure which is not necessarily prior art.
The present disclosure relates to systems and methods for evaluating electronic documents to identify and prioritize issues indicated by the text of the documents.
Collections of “big data” are becoming commonplace in modern industry. However, these large stores of data are useless without the employment of effective techniques for uncovering meaningful, actionable insights out of the multitude of available information. Most recently, analysis trends in business data science center on predictive analysis, allowing data owners to get ahead of the issues that may affect their business.
As trends intensify and issues become more volatile, the time that managers have to react to these issues diminishes, and their available options dwindle quickly. There is a need for systems and methods which address this problem for managers by providing an early warning of trending issues to maximize managerial control while limiting losses and missed opportunities.
If the process of sifting through mountains of data to select and prioritize issues for each report was done solely by hand, it would 1) take an inordinate amount of time, 2) have a high potential for error, and 3) undoubtedly introduce some measure of subjectivity. As these drawbacks are quite significant, there is a need for a solution which will automatically perform the task of issue discovery and prioritization in a consistent, repeatable fashion with very little human intervention.